{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 南瓜定价\n",
    "\n",
    "加载所需的库和数据集。将数据转换为包含以下子集的数据框：\n",
    "\n",
    "- 仅获取按蒲式耳定价的南瓜  \n",
    "- 将日期转换为月份  \n",
    "- 计算高价和低价的平均价格  \n",
    "- 将价格转换为按蒲式耳数量反映的定价  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City Name</th>\n",
       "      <th>Type</th>\n",
       "      <th>Package</th>\n",
       "      <th>Variety</th>\n",
       "      <th>Sub Variety</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Date</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Mostly Low</th>\n",
       "      <th>...</th>\n",
       "      <th>Unit of Sale</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Appearance</th>\n",
       "      <th>Storage</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Repack</th>\n",
       "      <th>Trans Mode</th>\n",
       "      <th>Unnamed: 24</th>\n",
       "      <th>Unnamed: 25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/29/17</td>\n",
       "      <td>270.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5/6/17</td>\n",
       "      <td>270.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>HOWDEN TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/24/16</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>HOWDEN TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/24/16</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24 inch bins</td>\n",
       "      <td>HOWDEN TYPE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11/5/16</td>\n",
       "      <td>90.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
       "\n",
       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
       "\n",
       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd # type: ignore\n",
    "import matplotlib.pyplot as plt # type: ignore\n",
    "import numpy as np # type: ignore\n",
    "from datetime import datetime\n",
    "\n",
    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
    "\n",
    "pumpkins.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>DayOfYear</th>\n",
       "      <th>Variety</th>\n",
       "      <th>City</th>\n",
       "      <th>Package</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>9</td>\n",
       "      <td>267</td>\n",
       "      <td>PIE TYPE</td>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>1 1/9 bushel cartons</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>9</td>\n",
       "      <td>267</td>\n",
       "      <td>PIE TYPE</td>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>1 1/9 bushel cartons</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>10</td>\n",
       "      <td>274</td>\n",
       "      <td>PIE TYPE</td>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>1 1/9 bushel cartons</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>10</td>\n",
       "      <td>274</td>\n",
       "      <td>PIE TYPE</td>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>1 1/9 bushel cartons</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>10</td>\n",
       "      <td>281</td>\n",
       "      <td>PIE TYPE</td>\n",
       "      <td>BALTIMORE</td>\n",
       "      <td>1 1/9 bushel cartons</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.636364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  DayOfYear   Variety       City               Package  Low Price  \\\n",
       "70      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
       "71      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
       "72     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
       "73     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       17.0   \n",
       "74     10        281  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
       "\n",
       "    High Price      Price  \n",
       "70        15.0  13.636364  \n",
       "71        18.0  16.363636  \n",
       "72        18.0  16.363636  \n",
       "73        17.0  15.454545  \n",
       "74        15.0  13.636364  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
    "\n",
    "columns_to_select = ['Package', 'Variety', 'City Name', 'Low Price', 'High Price', 'Date']\n",
    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
    "\n",
    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
    "\n",
    "\n",
    "# Specify the date format explicitly to avoid warnings\n",
    "# 数据中的年份是两位数格式（\"16\"），而 \"%Y\" 需要四位数年份。你需要将日期格式改为 \"%m/%d/%y\"（小写的 y 表示两位数年份）。\n",
    "# date_format = \"%m/%d/%y\"  # Replace with the actual format of your 'Date' column\n",
    "# pumpkins['Date'] = pd.to_datetime(pumpkins['Date'], format=date_format)\n",
    "# day_of_year = pumpkins['Date'].dt.dayofyear\n",
    "\n",
    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
    "# 原微软官方的\n",
    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
    "\n",
    "\n",
    "new_pumpkins = pd.DataFrame(\n",
    "    {'Month': month, \n",
    "     'DayOfYear' : day_of_year, \n",
    "     'Variety': pumpkins['Variety'], \n",
    "     'City': pumpkins['City Name'], \n",
    "     'Package': pumpkins['Package'], \n",
    "     'Low Price': pumpkins['Low Price'],\n",
    "     'High Price': pumpkins['High Price'], \n",
    "     'Price': price})\n",
    "\n",
    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
    "\n",
    "new_pumpkins.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A basic scatterplot reminds us that we only have month data from August through December. We probably need more data to be able to draw conclusions in a linear fashion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24ada299c50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter('Month','Price',data=new_pumpkins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24ada361290>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
